SOTAVerified

Multimodal Deep Learning

Multimodal deep learning is a type of deep learning that combines information from multiple modalities, such as text, image, audio, and video, to make more accurate and comprehensive predictions. It involves training deep neural networks on data that includes multiple types of information and using the network to make predictions based on this combined data.

One of the key challenges in multimodal deep learning is how to effectively combine information from multiple modalities. This can be done using a variety of techniques, such as fusing the features extracted from each modality, or using attention mechanisms to weight the contribution of each modality based on its importance for the task at hand.

Multimodal deep learning has many applications, including image captioning, speech recognition, natural language processing, and autonomous vehicles. By combining information from multiple modalities, multimodal deep learning can improve the accuracy and robustness of models, enabling them to perform better in real-world scenarios where multiple types of information are present.

Papers

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Showing 51–60 of 213 papers

TitleStatusHype
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing DataCode0
Describe-and-Dissect: Interpreting Neurons in Vision Networks with Language Models—0
Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting—0
MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal FusionCode1
A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection—0
Restoring Ancient Ideograph: A Multimodal Multitask Neural Network ApproachCode0
Multimodal deep learning approach to predicting neurological recovery from coma after cardiac arrest—0
DeepSeek-VL: Towards Real-World Vision-Language UnderstandingCode7
Cultural-Aware AI Model for Emotion RecognitionCode0
Multimodal Learning To Improve Cardiac Late Mechanical Activation Detection From Cine MR Images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Two Branch Network (Text - Bert + Image - Nts-Net)Accuracy96.81—Unverified